Characterization of oral and gut microbiome and plasma metabolomics in COVID-19 patients after 1-year follow-up

Background Due to the outbreak and rapid spread of coronavirus disease 2019 (COVID-19), more than 160 million patients have become convalescents worldwide to date. Significant alterations have occurred in the gut and oral microbiome and metabonomics of patients with COVID-19. However, it is unknown whether their characteristics return to normal after the 1-year recovery. Methods We recruited 35 confirmed patients to provide specimens at discharge and one year later, as well as 160 healthy controls. A total of 497 samples were prospectively collected, including 219 tongue-coating, 129 stool and 149 plasma samples. Tongue-coating and stool samples were subjected to 16S rRNA sequencing, and plasma samples were subjected to untargeted metabolomics testing. Results The oral and gut microbiome and metabolomics characteristics of the 1-year convalescents were restored to a large extent but did not completely return to normal. In the recovery process, the microbial diversity gradually increased. Butyric acid-producing microbes and Bifidobacterium gradually increased, whereas lipopolysaccharide-producing microbes gradually decreased. In addition, sphingosine-1-phosphate, which is closely related to the inflammatory factor storm of COVID-19, increased significantly during the recovery process. Moreover, the predictive models established based on the microbiome and metabolites of patients at the time of discharge reached high efficacy in predicting their neutralizing antibody levels one year later. Conclusions This study is the first to characterize the oral and gut microbiome and metabonomics in 1-year convalescents of COVID-19. The key microbiome and metabolites in the process of recovery were identified, and provided new treatment ideas for accelerating recovery. And the predictive models based on the microbiome and metabolomics afford new insights for predicting the recovery situation which benefited affected individuals and healthcare. Supplementary Information The online version contains supplementary material available at 10.1186/s40779-022-00387-y.

process. The collected tongue coating, fecal, and plasma samples from participants in hospital will be used for scientific research. These results and data from the hospital electronic medical records will provide auxiliary data for clinical diagnosis and treatment, and will be used for scientific research. Thank you for your corporation.
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3. The collected tongue coating, fecal, and serum samples will be used for scientific research.
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Inclusion and exclusion criteria
Nucleic acid test results were all positive when the convalescent patients were admitted 1-year ago. The inclusion criteria for healthy volunteers were similar to our previous studies [1]. Healthy controls who had the following diseases were excluded: coeliac disease, nonalcoholic fatty liver disease, irritable bowel syndrome (IBD), diabetes, metabolic syndrome and hypertension and oral disease. All participants who received antibiotics and/or probiotics within 8 weeks before providing samples were also excluded. All participants who have received the COVID-19 vaccine were excluded.

Tongue-coating collection
Each participant provided a tongue-coating sample from 7 am to 9 am. Tongue-coating samples were collected as our previous study [1]. On the day of sampling, the participants were asked to eat and brush their teeth after providing tongue-coating samples. The participants rinsed their mouths twice with sterile water before taking the tongue-coating samples. A professional operator used a pharyngeal swab to scrape the posterior middle to the anterior middle region of the tongue-coating.

DNA extraction
Tongue-coating and faecal microbial DNA was extracted by the Qiagen Mini Kit (Qiagen, Hilden, Germany) as described previously [2]. The samples were processed by phenol trichloromethane DNA extraction using a bead beater to mechanically disrupt cells, followed by phenol-chloroform extraction. Then, we purified the DNA according to the manufacturer's instructions. The DNAs were quantified by the Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA), and molecular size was estimated using agarose gel electrophoresis. All microbial DNAs were diluted to 10 ng/μL for microbial analysis.

PCR amplification
The details of PCR amplification were performed according to our previous study [1].
PCR primers incorporated sample-specific barcodes for multiplex sequencing using the Illumina MiSeq System (paired-end 250-nt reads). Amplify the extracted DNA samples  PicoGreen, Invitrogen) to quantify the products.

Sequence data process
The amplified reads were processed according to the following steps: (1)

Microbial diversity and taxonomic analysis
We calculated the microbial diversity according to sampling OTUs analysis.  [4].

Construction of POD
A fivefold cross-validation was conducted on a random forest model by the abundance profile of the optimal OTUs markers (R 3.4.1, randomForest 4.6 -12 package). The cross-validation error curve was calculated through five trials of the fivefold cross validation. The cut-off point through the minimum error plus the standard deviation (SD) was defined as the point with the minimum cross-validation error. The optimal OTUs was defined by the smallest number of OTUs sets with the error less than the cut-off value. In addition, the POD index was calculated by the optimal set of OTUs.
We used the receiver operating characteristic (ROC) curve to evaluate the model through the R package pROC. The AUC value indicated the the ROC effect. and negative ion mode (ESI -), respectively.

Metabolomics data preprocessing and annotation
The IS was used to evaluate the stability of the instrument. We calculated the minimum metabolic value for the specific samples whose metabolic level was lower than the quantitative lower limit. Then, we normalized the sum of all metabolic characteristics.
Metabolic characteristics whose QC was greater than 30% relative standard deviation (RSD) were discarded. In order to select metabolite with significant differences, statistical analysis of log10-converted data was conducted after normalization and imputation. These metabolic characteristics were identified by precise mass spectrometry. We performed the multivariate data analysis, including Pareto-scaled principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA).

Immunoassay of SARS-CoV-2 neutralizing antibody and IgG
Levels of neutralizing antibody against SARS-CoV-2 in serum were tested through the ELISA using kits (GenScript Biotech., Ltd., Nanjing, China) [5]. control) × 100%. The cutoff value of the kit was 30% (a rate ≥ 30% was defined as positive, and a rate < 30% was defined as negative).
Levels of IgG against SARS-CoV-2 in serum were tested through the direct chemiluminometric microparticle technology using kits (YHLO Biotech Co., Ltd., Shenzhen, China) [6]. The iFlash 3000-C chemiluminescence immunoassay analyzer (Shenzhen YHLO Biotech Co., Ltd., China) was used for the test. The positive judgment value of the kit was 10 U/ml (a value > 10 U/ml was defined as positive, and a value < 10 U/ml was defined as negative). The IgG levels were calculated as log10 (value).